code: def _sample_decomposition(coeffs, ops, time, n=1, seed=None):
      """Generate the randomly sampled decomposition
  
      Args:
          coeffs (array): the coefficients of the operations from each term in the Hamiltonian
          ops (list[~.Operator]): the normalized operations from each term in the Hamiltonian
          time (float): time to evolve under the target Hamiltonian
          n (int): number of samples in the product, defaults to 1
          seed (int): random seed. defaults to None
  
      Returns:
          list[~.Operator]: the decomposition of operations as per the approximation
      """
      normalization_factor = qml.math.sum(qml.math.abs(coeffs))
      probs = qml.math.abs(coeffs) / normalization_factor
      exps = [
          qml.exp(
              base,
              (coeff / qml.math.abs(coeff))
              * normalization_factor
              * time
              * 1j
              / n,
          )
          for base, coeff in zip(ops, coeffs)
      ]
  
      choice_rng = qml.math.random.default_rng(seed)
      return tuple(
          choice_rng.choice(exps, p=probs, size=n, replace=True)
      )
  
file_path: pennylane/pennylane/templates/subroutines/qdrift.py
imports: import pennylane
  from pennylane.operation import Operation
  from pennylane.math import requires_grad, unwrap
  from pennylane.ops import Sum, SProd, Hamiltonian
label_desc: Use image node_id 1 for calling a global function with example usage: _sample_decomposition(coeffs, ops, time, n, seed) and returns: tuple